OpenRaiser/Intern-Atlas huggingface dataset:
Recovery of signals by a weighted $\ell_2/\ell_1$ minimization under arbitrary prior support information
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we introduce a weighted $\ell_2/\ell_1$ minimization to recover block sparse signals with arbitrary prior support information. When partial prior support information is available, a sufficient condition based on the high order block RIP is derived to guarantee stable and robust recovery of block sparse signals via the weighted $\ell_2/\ell_1$ minimization. We then show if the accuracy of arbitrary prior block support estimate is at least $50\%$, the sufficient recovery condition by the weighted $\ell_2/\ell_{1}$ minimization is weaker than that by the $\ell_2/\ell_{1}$ minimization, and the weighted $\ell_2/\ell_{1}$ minimization provides better upper bounds on the recovery error in terms of the measurement noise and the compressibility of the signal. Moreover, we illustrate the advantages of the weighted $\ell_2/\ell_1$ minimization approach in the recovery performance of block sparse signals under uniform and non-uniform prior information by extensive numerical experiments. The significance of the results lies in the facts that making explicit use of block sparsity and partial support information of block sparse signals can achieve better recovery performance than handling the signals as being in the conventional sense, thereby ignoring the additional structure and prior support information in the problem.
Simultaneous Lightwave Information and Power Transfer (SLIPT) for Indoor IoT Applications
Venue: arXiv cs.IT 2017
Year: 2017

We present the concept of Simultaneous Lightwave Information and Power Transfer (SLIPT) for indoor Internet-of-Things (IoT) applications. Specifically, we propose novel and fundamental SLIPT strategies, which can be implemented through Visible Light or Infrared communication systems, equipped with a simple solar panel-based receiver. These strategies are performed at the transmitter or at the receiver, or at both sides, named Adjusting transmission, Adjusting reception and Coordinated adjustment of transmission and reception, correspondingly. Furthermore, we deal with the fundamental trade-off between harvested energy and quality-of-service (QoS), by maximizing the harvested energy, while achieving the required user's QoS. To this end, two optimization problems are formulated and optimally solved. Computer simulations validate the optimum solutions and reveal that the proposed strategies considerably increase the harvested energy, compared to SLIPT with fixed policies.
Joint Optimal Pricing and Electrical Efficiency Enforcement for Rational Agents in Micro Grids
Venue: arXiv cs.SY 2017
Year: 2017

In electrical distribution grids, the constantly increasing number of power generation devices based on renewables demands a transition from a centralized to a distributed generation paradigm. In fact, power injection from Distributed Energy Resources (DERs) can be selectively controlled to achieve other objectives beyond supporting loads, such as the minimization of the power losses along the distribution lines and the subsequent increase of the grid hosting capacity. However, these technical achievements are only possible if alongside electrical optimization schemes, a suitable market model is set up to promote cooperation from the end users. In contrast with the existing literature, where energy trading and electrical optimization of the grid are often treated separately or the trading strategy is tailored to a specific electrical optimization objective, in this work we consider their joint optimization. Specifically, we present a multi-objective optimization problem accounting for energy trading, where: 1) DERs try to maximize their profit, resulting from selling their surplus energy, 2) the loads try to minimize their expense, and 3) the main power supplier aims at maximizing the electrical grid efficiency through a suitable discount policy. This optimization problem is proved to be non convex, and an equivalent convex formulation is derived. Centralized solutions are discussed first, and are subsequently distributed. Numerical results to demonstrate the effectiveness of the so obtained optimal policies are then presented.
Kinematics and workspace analysis of a 3ppps parallel robot with u-shaped base
Venue: arXiv cs.RO 2017
Year: 2017

This paper presents the kinematic analysis of the 3-PPPS parallel robot with an equilateral mobile platform and a U-shape base. The proposed design and appropriate selection of parameters allow to formulate simpler direct and inverse kinematics for the manipulator under study. The parallel singularities associated with the manipulator depend only on the orientation of the end-effector, and thus depend only on the orientation of the end effector. The quaternion parameters are used to represent the aspects, i.e. the singularity free regions of the workspace. A cylindrical algebraic decomposition is used to characterize the workspace and joint space with a low number of cells. The dis-criminant variety is obtained to describe the boundaries of each cell. With these simplifications, the 3-PPPS parallel robot with proposed design can be claimed as the simplest 6 DOF robot, which further makes it useful for the industrial applications.
Speaking Style Authentication Using Suprasegmental Hidden Markov Models
Venue: arXiv cs.SD 2017
Year: 2017

The importance of speaking style authentication from human speech is gaining an increasing attention and concern from the engineering community. The importance comes from the demand to enhance both the naturalness and efficiency of spoken language human-machine interface. Our work in this research focuses on proposing, implementing, and testing speaker-dependent and text-dependent speaking style authentication (verification) systems that accept or reject the identity claim of a speaking style based on suprasegmental hidden Markov models (SPHMMs). Based on using SPHMMs, our results show that the average speaking style authentication performance is: 99%, 37%, 85%, 60%, 61%, 59%, 41%, 61%, and 57% belonging respectively to the speaking styles: neutral, shouted, slow, loud, soft, fast, angry, happy, and fearful.
Employing both Gender and Emotion Cues to Enhance Speaker Identification Performance in Emotional Talking Environments
Venue: arXiv cs.SD 2017
Year: 2017

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22%, 4.45%, and 19.56%, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35% of that obtained in subjective evaluation by human judges.
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation
Venue: arXiv cs.CL 2017
Year: 2017

Automated metrics such as BLEU are widely used in the machine translation literature. They have also been used recently in the dialogue community for evaluating dialogue response generation. However, previous work in dialogue response generation has shown that these metrics do not correlate strongly with human judgment in the non task-oriented dialogue setting. Task-oriented dialogue responses are expressed on narrower domains and exhibit lower diversity. It is thus reasonable to think that these automated metrics would correlate well with human judgment in the task-oriented setting where the generation task consists of translating dialogue acts into a sentence. We conduct an empirical study to confirm whether this is the case. Our findings indicate that these automated metrics have stronger correlation with human judgments in the task-oriented setting compared to what has been observed in the non task-oriented setting. We also observe that these metrics correlate even better for datasets which provide multiple ground truth reference sentences. In addition, we show that some of the currently available corpora for task-oriented language generation can be solved with simple models and advocate for more challenging datasets.
Fundamental Limits and Tradeoffs in Autocatalytic Pathways
Venue: arXiv cs.SY 2017
Year: 2017

This paper develops some basic principles to study autocatalytic networks and exploit their structural properties in order to characterize their inherent fundamental limits and tradeoffs. In a dynamical system with autocatalytic structure, the system's output is necessary to catalyze its own production. We consider a simplified model of Glycolysis pathway as our motivating application. First, the properties of these class of pathways are investigated through a simplified two-state model, which is obtained by lumping all the intermediate reactions into a single intermediate reaction. Then, we generalize our results to autocatalytic pathways that are composed of a chain of enzymatically catalyzed intermediate reactions. We explicitly derive a hard limit on the minimum achievable $\mathcal L_2$-gain disturbance attenuation and a hard limit on its minimum required output energy. Finally, we show how these resulting hard limits lead to some fundamental tradeoffs between transient and steady-state behavior of the network and its net production.
Cooperative Slotted ALOHA for Massive M2M Random Access Using Directional Antennas
Venue: arXiv cs.IT 2017
Year: 2017

Slotted ALOHA (SA) algorithms with Successive Interference Cancellation (SIC) decoding have received significant attention lately due to their ability to dramatically increase the throughput of traditional SA. Motivated by increased density of cellular radio access networks due to the introduction of small cells, and dramatic increase of user density in Machine-to-Machine (M2M) communications, SA algorithms with SIC operating cooperatively in multi base station (BS) scenario are recently considered. In this paper, we generalize our previous work on Slotted ALOHA with multiple-BS (SA-MBS) by considering users that use directional antennas. In particular, we focus on a simple randomized beamforming strategy where, for every packet transmission, a user orients its main beam in a randomly selected direction. We are interested in the total achievable system throughput for two decoding scenarios: i) non-cooperative scenario in which traditional SA operates at each BS independently, and ii) cooperative SA-MBS in which centralized SIC-based decoding is applied over all received user signals. For both scenarios, we provide upper system throughput limits and compare them against the simulation results. Finally, we discuss the system performance as a function of simple directional antenna model parameters applied in this paper.
Automatic Face Image Quality Prediction
Venue: arXiv cs.CV 2017
Year: 2017

Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this work, we propose (and compare) two methods for automatic face image quality based on target face quality values from (i) human assessments of face image quality (matcher-independent), and (ii) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, LFW and IJB-A, which both contain facial variations with multiple quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.
Grasp Pose Detection in Point Clouds
Venue: arXiv cs.RO 2017
Year: 2017

Recently, a number of grasp detection methods have been proposed that can be used to localize robotic grasp configurations directly from sensor data without estimating object pose. The underlying idea is to treat grasp perception analogously to object detection in computer vision. These methods take as input a noisy and partially occluded RGBD image or point cloud and produce as output pose estimates of viable grasps, without assuming a known CAD model of the object. Although these methods generalize grasp knowledge to new objects well, they have not yet been demonstrated to be reliable enough for wide use. Many grasp detection methods achieve grasp success rates (grasp successes as a fraction of the total number of grasp attempts) between 75% and 95% for novel objects presented in isolation or in light clutter. Not only are these success rates too low for practical grasping applications, but the light clutter scenarios that are evaluated often do not reflect the realities of real world grasping. This paper proposes a number of innovations that together result in a significant improvement in grasp detection performance. The specific improvement in performance due to each of our contributions is quantitatively measured either in simulation or on robotic hardware. Ultimately, we report a series of robotic experiments that average a 93% end-to-end grasp success rate for novel objects presented in dense clutter.
Automated Audio Captioning with Recurrent Neural Networks
Venue: arXiv cs.SD 2017
Year: 2017

We present the first approach to automated audio captioning. We employ an encoder-decoder scheme with an alignment model in between. The input to the encoder is a sequence of log mel-band energies calculated from an audio file, while the output is a sequence of words, i.e. a caption. The encoder is a multi-layered, bi-directional gated recurrent unit (GRU) and the decoder a multi-layered GRU with a classification layer connected to the last GRU of the decoder. The classification layer and the alignment model are fully connected layers with shared weights between timesteps. The proposed method is evaluated using data drawn from a commercial sound effects library, ProSound Effects. The resulting captions were rated through metrics utilized in machine translation and image captioning fields. Results from metrics show that the proposed method can predict words appearing in the original caption, but not always correctly ordered.
Providing Effective Real-time Feedback in Simulation-based Surgical Training
Venue: arXiv cs.AI 2017
Year: 2017

Virtual reality simulation is becoming popular as a training platform in surgical education. However, one important aspect of simulation-based surgical training that has not received much attention is the provision of automated real-time performance feedback to support the learning process. Performance feedback is actionable advice that improves novice behaviour. In simulation, automated feedback is typically extracted from prediction models trained using data mining techniques. Existing techniques suffer from either low effectiveness or low efficiency resulting in their inability to be used in real-time. In this paper, we propose a random forest based method that finds a balance between effectiveness and efficiency. Experimental results in a temporal bone surgery simulation show that the proposed method is able to extract highly effective feedback at a high level of efficiency.
A solution for secure use of Kibana and Elasticsearch in multi-user environment
Venue: arXiv cs.CR 2017
Year: 2017

Monitoring is indispensable to check status, activities, or resource usage of IT services. A combination of Kibana and Elasticsearch is used for monitoring in many places such as KEK, CC-IN2P3, CERN, and also non-HEP communities. Kibana provides a web interface for rich visualization, and Elasticsearch is a scalable distributed search engine. However, these tools do not support authentication and authorization features by default. In the case of single Kibana and Elasticsearch services shared among many users, any user who can access Kibana can retrieve other's information from Elasticsearch. In multi-user environment, in order to protect own data from others or share part of data among a group, fine-grained access control is necessary. The CERN cloud service group had provided cloud utilization dashboard to each user by Elasticsearch and Kibana. They had deployed a homemade Elasticsearch plugin to restrict data access based on a user authenticated by the CERN Single Sign On system. It enabled each user to have a separated Kibana dashboard for cloud usage, and the user could not access to other's one. Based on the solution, we propose an alternative one which enables user/group based Elasticsearch access control and Kibana objects separation. It is more flexible and can be applied to not only the cloud service but also the other various situations. We confirmed our solution works fine in CC-IN2P3. Moreover, a pre-production platform for CC-IN2P3 has been under construction. We will describe our solution for the secure use of Kibana and Elasticsearch including integration of Kerberos authentication, development of a Kibana plugin which allows Kibana objects to be separated based on user/group, and contribution to Search Guard which is an Elasticsearch plugin enabling user/group based access control. We will also describe the effect on performance from using Search Guard.
Chatbots as Conversational Recommender Systems in Urban Contexts
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, we outline the vision of chatbots that facilitate the interaction between citizens and policy-makers at the city scale. We report the results of a co-design session attended by more than 60 participants. We give an outlook of how some challenges associated with such chatbot systems could be addressed in the future.
Statistical Analysis of Dice CAPTCHA Usability
Venue: arXiv cs.HC 2017
Year: 2017

In this paper the elements of the CAPTCHA usability are analyzed. CAPTCHA, as a time progressive element in computer science, has been under constant interest of ordinary, professional as well as the scientific users of the Internet. The analysis is given based on the usability elements of CAPTCHA which are abbreviated as user-centric approach to the CAPTCHA. To demonstrate it, the specific type of Dice CAPTCHA is used in the experiment. The experiment is conducted on 190 Internet users with different demographic characteristics on laptop and tablet computers. The obtained results are statistically processed. At the end, the results are compared and conclusion of their use is drawn.
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
Venue: arXiv cs.IR 2017
Year: 2017

Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals. We argue that the context of these matching signals is also important. Intuitively, when extracting, modeling, and combining matching signals, one would like to consider the surrounding text (local context) as well as other signals from the same document that can contribute to the overall relevance score. In this work, we highlight three potential shortcomings caused by not considering context information and propose three neural ingredients to address them: a disambiguation component, cascade k-max pooling, and a shuffling combination layer. Incorporating these components into the PACRR model yields Co-PACRR, a novel context-aware neural IR model. Extensive comparisons with established models on Trec Web Track data confirm that the proposed model can achieve superior search results. In addition, an ablation analysis is conducted to gain insights into the impact of and interactions between different components. We release our code to enable future comparisons.
DataLair: Efficient Block Storage with Plausible Deniability against Multi-Snapshot Adversaries
Venue: arXiv cs.CR 2017
Year: 2017

Sensitive information is present on our phones, disks, watches and computers. Its protection is essential. Plausible deniability of stored data allows individuals to deny that their device contains a piece of sensitive information. This constitutes a key tool in the fight against oppressive governments and censorship. Unfortunately, existing solutions, such as the now defunct TrueCrypt, can defend only against an adversary that can access a users device at most once (single-snapshot adversary). Recent solutions have traded significant performance overheads for the ability to handle more powerful adversaries, that are able to access the device at multiple points in time (multi-snapshot adversary). In this paper we show that this sacrifice is not necessary. We introduce and build DataLair, a practical plausible deniability mechanism. When compared with existing approaches, DataLair is two orders of magnitude faster for public data accesses, and 5 times faster for hidden data accesses. An important component in DataLair is a new write-only ORAM construction which improves on the complexity of the state of the art write-only ORAM by a factor of O(logN ), where N denotes the underlying storage disk size.
Small Profits and Quick Returns: A Practical SocialWelfare Maximizing Incentive Mechanism for Deadline-Sensitive Tasks in Crowdsourcing
Venue: arXiv cs.HC 2017
Year: 2017

As the driving force of crowdsourcing is the interaction among participants, various incentive mechanisms have been proposed to attract sufficient participants. However, the existing works assume that all the providers always meet the deadline and the task value accordingly remains constant. To bridge the gap of such impractical assumption, we model the heterogeneous punctuality behavior of providers and the task value depreciation of requesters. Based on those models, we propose an Expected Social Welfare Maximizing (ESWM) mechanism that aims to maximize the expected social welfare in polynomial time. Simulation results show that our heuristic-based mechanism achieves higher expected social welfare and platform utility via attracting more participants.
Achieving Efficient and Provably Secure Steganography in Practice
Venue: arXiv cs.CR 2017
Year: 2017

Steganography is the task of concealing a message within a medium such that the presence of the hidden message cannot be detected. Though the prospect of steganography is conceivably interesting in many contexts, and though work has been done both towards formalizing steganographic security and providing provably secure constructions, little work exists attempting to provide efficient and provably secure steganographic schemes in specific, useful domains. Beginning from the starting point of the initial definition of steganographic security, I have engaged in an exploration which has developed to include two primary tasks, both pointing towards the realization of efficient and secure steganographic systems in practice: (a) investigating the syntactic and semantic applicability of the current formalism of steganographic security to a broader range of potentially interesting domains and (b) constructing and implementing provably secure (symmetric-key) steganographic schemes in domains which are well-suited to the current formalism.